Multi-night titration pressure determination

ABSTRACT

A multi-night titration (MNT) process to find an optimal single therapeutic pressure of a CPAP device. This single therapeutic pressure can then be used on an on-going basis by the patient after the titration period. The MNT process differs from current auto adjusting processes used for titration (or ongoing use) in that the MNT process does not respond locally by adjusting pressures to individual events. With existing devices, the continuous adjustment of supplied air pressure always responds to one or a small number of events and thus fails to compensate for a patient&#39;s adaptation thereto, resulting in the supply of a less than optimal therapeutic pressure to the patient. While auto adjusting processes often capture and respond well to short-term and transient conditions, the MNT process of the current disclosure seeks to capture long term trends and find the most suitable average single pressure for a patient.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a national phase of PCT Application No.PCT/US2014/022633, filed Mar. 10, 2014, entitled “Multi-Night TitrationPressure Determination,” which claims priority to U.S. ProvisionalPatent Application No. 61/793,070, entitled “Methods for CalculatingMulti-Night Titration Pressure,” filed Mar. 15, 2013, which isincorporated by reference herein in its entirety.

PARTIES OF JOINT RESEARCH AGREEMENT

The present disclosure was made as a result of activities undertakenwithin the scope of a written joint research agreement between Fisher &Paykel Healthcare Limited and New York University.

FIELD

The present disclosure relates to the field of continuous positiveairway pressure (CPAP) titrating devices.

BACKGROUND

Patients suffering from breathing disorders, including for example,sleep disordered breathing (SDB) and obstructive sleep apnea (OSA), areoften advised to use a continuous positive airway pressure (CPAP)device. CPAP devices use positive air pressure to keep the patientsairways from collapsing as they breathe. There are two main types ofCPAP devices. These include single or bi-level pressure CPAP devicesthat maintain a given pressure or set of pressures and auto titratingCPAP devices which rapidly change pressures in response to breathingevents.

When a patient first obtains a CPAP device, it is common for them tovisit a sleep clinic in order to determine appropriate pressure settingsfor their CPAP device. This is particularly true for single pressure orbi-level pressure CPAP devices, but can also include auto titratingdevices. Sleep clinic evaluations can be costly and often require anovernight stay at the sleep clinic. Moreover, patients often visit asleep clinic for a very short period of time, such as a single night,resulting in only limited information about the patient. Even after asleep clinic visit, patient's often experience discomfort if theselected pressure for the CPAP device is not optimal. This necessitatesa costly return visit to a sleep clinic.

Auto-titrating CPAP devices attempt to alleviate this problem bymonitoring breathing patterns of a patient and then rapidly andautomatically adjusting a pressure supplied to the patient based on theoccurrence of any abnormal breathing events, without waiting to see ifthe events recur at that pressure. However, patients often do nottolerate the use of continuously adapting pressures over extendedperiods of time or prefer a single pressure once that single pressure istitrated optimally.

SUMMARY

The present disclosure provides a multi-night titration (MNT) process tofind an optimal single therapeutic pressure of a CPAP device. Thissingle therapeutic pressure can then be used on an on-going basis by thepatient after the titration period. The MNT process differs from currentauto adjusting processes used for titration (or ongoing use) in that theMNT process does not respond locally by adjusting pressures toindividual events. With existing devices, the continuous adjustment ofsupplied air pressure always responds to one or a small number of eventsand thus fails to compensate for a patient's adaptation thereto,resulting in the supply of a less than optimal therapeutic pressure tothe patient. While auto adjusting processes often capture and respondwell to short-term and transient conditions, the MNT process of thecurrent disclosure seeks to capture long term trends and find the mostsuitable average single pressure for a patient.

The MNT process disclosed herein uses data collected at differentpressures over several sessions or several nights to find an optimaltherapeutic pressure by capturing short and long term trends and theeffects thereof. Embodiments of the MNT process according to thedisclosure seek to capture long term trends and find the most suitableaverage single pressure for a patient, taking into consideration changesthe patient may experience over time including position, sleep state,alcohol use etc. During the normal course of titration, the MNT processdoes not respond to individual respiratory events such as, for example,single apneas, hypopneas and short periods of inspiratory flowlimitation. The MNT process is adapted to automatically collect andanalyze patient data at sub-therapeutic, therapeutic andabove-therapeutic pressures to subsequently estimate the besttherapeutic pressure and accurately assess its overall effectivenessover sustained periods. As will be described in greater detail herein,the MNT process can be composed of either one or two steps.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an embodiment of a CPAP system.

FIG. 2 illustrates a schematic diagram of a CPAP device.

FIG. 3 depicts a flow chart of an MNT process.

FIG. 3A depicts a flow chart illustrating an interaction between a fasttitration system and a slow titration system.

FIG. 4 depicts an embodiment of a fast titration process.

FIG. 5 depicts an embodiment of a slow titration process.

FIG. 5A depicts further details of an embodiment of a slow titrationprocess.

FIG. 6 depicts a plot of a sleep disordered breathing index versuspressure.

FIG. 7 depicts an optimal pressure of the plot of FIG. 6, calculated viaa target crossing calculation.

FIG. 8 depicts an optimal pressure of the plot of FIG. 6, calculated viadetermination of an inflection point.

FIG. 9 depicts the selection of an optimal pressure of the plot of FIG.6, calculated via determination of slope change.

FIG. 10 depicts an optimal pressure of the plot of FIG. 6, calculatedvia determination of a minimum sleep disordered breathing index point.

FIG. 11 depicts determination of an average curve from the data in theplot in FIG. 6.

FIG. 12 depicts the determination of a minimum sleep disorderedbreathing index point using the average curve generated in FIG. 11.

FIG. 13 depicts the selection of an optimal pressure at a pressure belowa minimum pressure at a point after which a sleep disordered breathingindex does not improve significantly upon pressure increase.

FIG. 14 depicts a calculation of a margin of error at each suppliedpressure.

DETAILED DESCRIPTION

FIG. 1 illustrates an embodiment of a CPAP system 100 according to thepresent disclosure. The system includes a CPAP device 102, a tube 104and a mask 106. The mask 106, in use, is positioned over one or both ofa nose and face of the patient to supply a positive air pressurethereto. In an embodiment, the CPAP system 100 also includes a sensor108 positioned in one or both of the mask 106 and tube 104 in order tomonitor air flow there through. In an embodiment, the sensor is a flowsensor placed in the system before the blower. In an embodiment, thesensor is a pressure sensor placed in the system after the blower, butbefore the humidifier. As will be understood by those of skill in theart, the sensor can be any of a number of sensors located at variouslocations in the system that are capable of detecting operatingcharacteristics of the system and flow states of the patient.

FIG. 2 illustrates a schematic depiction of CPAP device 102. CPAP device102 includes a controller 201, an air supply 203, a memory device 205,and optionally a display 207 and user inputs 209. The controller 201controls the operation of the CPAP device 102. The controller 201 caninclude, for example, analog or digital processors or other electroniccontrol devices as would be understood by a person of skill in the artfrom the present disclosure. The controller 201 controls the operationof the air supply 203. The controller also receives and analyzes sensorsignals from sensor(s) 108. The controller 201 communicates with thememory device 205 to store information including operating information,sensor information, and other information as would be understood by aperson of skill in the art and as disclosed herein. The controller canalso optionally receive commands from a user input 209 as well ascommunicate output information to the display 207.

FIG. 3 illustrates an embodiment of a MNT process 300 according to thepresent disclosure. The MNT process 300 can either begin with anoptional fast titration process 301 or an initial clinician set ordefault pressure 303. The fast titration process 301 is carried out toquickly determine, based on short term events, an initial therapeuticpressure close to optimal therapeutic levels. The optional fasttitration process 301 avoids the need for the patient to experiencepressures significantly different from the optimal therapeutic level forlong periods of time, thereby reducing discomfort.

Once an initial pressure is determined, either by the fast titrationprocess 301 or through a clinician/default set value 303, the MNTprocess 300 proceeds to a slow titration process 305. The slow titrationprocess 305 determines secondary therapeutic pressure based oninformation from multiple sessions (each potentially several hourslong). These sessions operate at pressures (1) below the initialtherapeutic pressure, (2) at the initial therapeutic pressure and (3)above the initial therapeutic pressure. The information from thesesessions is then stored in a storage device, such as memory device 205,and further integrated into a single secondary prescription pressure forongoing treatment by a processing arrangement, such as, for example,controller 201.

In an embodiment, the fast titration process 301 and slow titrationprocess 305 may operate either sequentially or in parallel, depending onthe configuration and can occur recursively if periods of severelyelevated sleep disordered breathing are unexpectedly detected during theslow titration process 305. This type of recursive system is illustratedin FIG. 3A. In another embodiment, the slow titration process 305 may beemployed independently of the fast titration process 301 as the sole MNTtitration process.

Embodiments of the MNT processes, including the fast titration and slowtitration processes, disclosed herein are based on one or severalindices of sleep disordered breathing (SDB). These indices indicate thefrequency and/or severity of the sleep disordered breathing over thecollection interval and are used to determine future pressureadjustments.

In the fast titration process, the collection intervals are relativelyshort and intended to move a patient quickly to an acceptable pressureto begin the slow titration process.

In the slow titration process, the collection intervals are chosen to belong enough to be representative of the individual's long term behavior.This avoids the pitfalls of quickly adjusting pressure at the occurrenceof small clusters of events as is typically done by conventionalautotitrating CPAP processes.

Examples of appropriate SDB indices include, but are not limited to,apnea-hypopnea index (AHI), respiratory disturbance index (RDI), percentsustained flow limitation (% SFL), and one or more obstruction indices(OI).

AHI captures the rate of occurrence of typical apneas and hypopneas asan Index over time (events/hour).

RDI captures the rate of occurrence of apneas, hypopneas, and moresubtle events like respiratory-effort related arousals (RERA's) as anindex over time.

% SFL corresponds to a percentage of time spent or percentage of breathswhere there is evidence of elevated upper airway resistance orcollapsibility (i.e., sustained flow limitation evidenced by the shapeof the inspiratory airflow contour).

One or more OIs are usually mathematical combinations of the AHI and %SFL (for example AHI+⅓*% SFL).

Other useful information which can also be captured and used in the MNTprocess can include awake time, sleep state and leakage.

While the simplest process for determining an optimal MNT pressure(P_(MNT)), each time the MNT process is performed, it uses only a singleone of the above SDB indices, it is also possible to combine or usedifferent SDB indices for different parts of the process or acombination of indices for every or various parts of the process. Forexample, the fast titration process may titrate on AHI, whereas the slowtitration process may titrate on OI. Thus, it is noted that the presentdisclosure covers any combination of SDB indices with any of thedisclosed processes. Furthermore, during the operation of the slowtitration process acting on one index (for example the OI), a concurrentmonitoring of another index (such as the AHI, which typically measuresthe more severe breathing events) can be used to modify or abort theslow titration process when this second sleep disordered breathing indexrises above a predetermined limit for at least a predetermined timeperiod. The predetermined limit can be, for example, a limit indicativeof severe impairment. The predetermined time period can be, for example,one hour.

An embodiment of the fast titration process 301 is described in greaterdetail with respect to FIG. 4. The fast titration process responds tosmall numbers of individual events or to relatively short periods ofdata collection (such as, for example, 1 hour) and integrates multipleevents into a sleep disordered breathing index (such as, for example, anOI). The fast titration process responds to severe sleep disorderedbreathing as this occurs by raising CPAP pressure. The goal is to raisethe pressure into a range that reduces severity of the sleep disorderedbreathing from severe to mild-moderate, as those skilled in the art willunderstand. The fast titration processes can be implemented by analyzingone or several SDB indices (SDBIndex_(w)). As described above, theseindices are analyzed over a relatively short time window w and the fasttitration process changes the pressure repeatedly based on theserelatively short durations. Specifically, referring to FIG. 4, startingat step 401, the system determines an SDBIndex_(w) over a given timewindow. Once the index is determined, the system moves to step 403 wherethe system determines whether an SDBIndex_(w) at the current indexpressure is greater than the targeted partially therapeutic SDB indexSDB_(Th). If SDBIndex_(w) is greater (i.e., the pressure is stillsub-therapeutic), the method proceeds to step 405 where the suppliedpressure is increased (or decreased) by a value ΔP. The system thenmoves to step 407 where fast titration exit criteria is analyzed todetermine if an appropriate pressure has been reached to begin the slowtitration process. If the exit criteria has not been reached at step407, the system returns to step 401 where the SDBIndex_(w) isre-determined at the new pressure over a new time window. If it isdetermined in step 403 that SDBIndex_(w) is not greater than SDB_(Th),the process can skip step 405 and proceed directly to step 407.

The process continues to loop until at least one exit criteria isreached. The fast titration process continues to adjust the supplied airpressure until an exit criterion is reached in step 407. In such a case,the fast titration process passes control to the slow titration process305. The slow titration process determines a secondary therapeuticpressure, as described in greater detail herein.

The exit criteria of step 407 may correspond to a condition where theprocess does not detect a need for further pressure changes. Forexample, the exit criteria can be that no net change in pressure occursfor a predetermined period of time (e.g., about 3 hours, an entirenight, etc.). In an embodiment, this can be longer than the length ofone individual data collection period. The exit criteria may alsocorrespond to a condition wherein the process changes the pressure bothup and down, but the supplied pressure remains below a determinedpressure for a specified fraction of the time (e.g., 80-90% of totaltitration time). In such a situation, the supplied pressure or apressure slightly below the supplied pressure is chosen as the start ofthe slow titration process 305.

The fast titration process 301 of FIG. 4 is configured to select apressure that treats the majority of the sleep disordered breathing by afirst relatively crude criterion. This results in a selection of aninitial therapeutic pressure within a predetermined acceptable range ofa final ideal therapeutic pressure. This range is intended to at leastpartially treat the patient by reducing the severity of sleep disorderedbreathing events. In an embodiment, the initial therapeutic pressure ischosen to be predictably below or near the final ideal therapeuticpressure. The initial therapeutic pressure is then refined by the slowtitration process 305, which is described in greater detail hereinafterwith respect to FIG. 5.

In an embodiment, the fast titration process is performed by anauto-adjusting or auto-titration device (collectively referred to hereinas an auto CPAP device). In an embodiment an auto CPAP device is usedfor a certain period of time by the patient, for example, apredetermined number of days. In an embodiment, the initial pressuresupplied to the slow titration process is based on the operation of theauto CPAP device. For example, in an embodiment, the 90^(th) percentile(or other percentile) pressure used by the CPAP device is determined andused as the initial pressure for the slow titration process. In anembodiment, a simple average CPAP pressure can be used as the initialpressure for the slow titration process. Other methods can be employedto determine the initial pressure based on the auto CPAP device'ssupplied pressure.

The slow titration process 305 is configured to refine, during itsperformance, the initial therapeutic pressure that has already beensupplied with the intent of determining a therapeutic pressure (P_(MNT))that is optimized for use in a sustained single CPAP prescription.

The main process for determination of P_(MNT) via the slow titrationprocess 305 is composed of four main parts that can be used as a singlepass or applied recursively. The slow titration process 305 includes thesteps of data collection to the point of having sufficient data overmultiple pressures (step 501), data analysis to determine the optimalpressure for prescription (step 503), verification of quality of result(i.e., an efficacy of the prescribed pressure) (step 505) and validationof effectiveness over additional time periods (step 507), as will bedescribed in greater detail below.

The data collection of step 501 occurs over multiple intervals of time.Each interval of time is designed to capture the entire expression ofsleep disordered breathing over more than one state rather thaninstantaneously. In other words, each interval is designed withsufficient time to gain a picture of the average value of OI over a fullsleep cycle and/or during multiple positions. As such, the datacollection periods are generally longer for each data point than in thefast titration process 301. The intervals can range from several hoursto a full day.

In an embodiment, the interval is in a range between about 1-2 hours andabout 8 hours or one full night. In an embodiment, the interval is about3 hours. In an embodiment, the range is greater than about 3 hours. Inan embodiment, the interval varies so that a predetermined minimum ofacceptable data is obtained for each interval. For example, if about 3hours of data is desired, it may take about 5 hours at a single pressureto obtain about 3 acceptable hours of data because about 2 hours may becorrupted by the patient waking up or the detection of air leakage at anunacceptable rate. Moreover, if an interval is interrupted, for example,by the patient discontinuing use of the machine, the interval can berestarted or simply continued when the patient returns to use of themachine. In an embodiment, data can also be collected in a mannerdesigned to anticipate a minimal value that would occur if the periodwere extended. For example, an OI of 45 in one hour anticipates aminimal OI value of 15 in about 3 hours. Thus, the process may beconfigured to abort a data collection period and extrapolate the resultbased on anticipatory results.

Any one or several determined SDB indices, SDBIndex_(w), may becollected over at least several time windows and should include at leastsome data collections at a range of pressures. For example, the range ofpressures can include a combination of pressures that are below theinitial therapeutic pressure, at the initial therapeutic pressure andabove the initial therapeutic pressure levels. This range of datacollection is chosen to include the pressure achieved by the fasttitration process 301 if performed. Alternatively, the slow titrationmay start at a predetermined level (e.g., 0 cm H₂O, 4 cm H₂O or aprescribed value) or clinician set level, if no fast titration has beenperformed. The range of pressures used to collect multiple data windowsmay then be modified to guarantee that the process collects data atleast at one pressure that is sub-therapeutic (below the initialtherapeutic pressure) and one that reaches into the therapeutic range(at pressures at least as great as the initial therapeutic pressure). Ofcourse other combinations of pressures can also be chosen. Moreover,multiple sessions can be performed at different time periods using thesame pressure interspersed with other pressures. For example, the systemcan start with a therapeutic pressure and move to a sub-therapeuticpressure and then return to a therapeutic pressure before moving to anabove therapeutic level.

In its simplest form, data collection is performed by collecting the SDBindices several times (for example, 3 times for about 3 hours at asingle pressure) for each of multiple pressures over a range ofpressures including and extending on either side of the initialpreliminary therapeutic pressure. Each data collection period lasts fora pre-defined time and results in a calculation of each relevantSDBIndex_(w). This collection of relevant SDBIndex_(w)s can then beaveraged for all time periods collected at that pressure or used asindividual data points in the data analysis. In an embodiment, thepressure levels and time periods are predefined relative to the initialpressure. The data for each collection period is not analyzed until alldata from each pressure range is collected for the appropriate amount oftime. In an embodiment, of such a collection system, escape criteriabased on a high SDB index value can be used to abort collection at aparticular pressure level.

In an embodiment, pressure levels can increase sequentially from asub-therapeutic level to an above therapeutic level. In such anembodiment, a first low pressure can be chosen relative to an initialpressure. Once collection at the first pressure is completed, the systemwill increase the pressure by a predetermined amount. For example, thispressure increase can be 1 cm H₂O. In other embodiments, the pressureincrease can be 0.5 cm H₂O or in a range between 0.1 cm H₂O to 2 cm H₂O.The data collection then continues to step through all desired pressurelevels sequentially until all data is collected at the predeterminedpressure levels for the predetermined period of time. The system canthem move to the data analysis step 503 to analyze the data. In anembodiment, the data analysis step 503 can also determine if sufficientacceptable data has been collected and can return the titration processback to step 501 if needed to collect additional data.

Alternatively, the slow titration process 305 can collect data from aninitial set of pressure levels and extend this pressure range based onthe results obtained. An example of this is described below in detailhereinafter. Specifically, in step 501, the initial data is collected atpressures referenced relative to the starting pressure P (such as, forexample, P−1, P, P+1, P+2, P+3). The SDB indices obtained from thesecollection periods are then examined. If the SDBIndex for the lowerpressure(s) is not above the therapeutic value (such as, for example,the lowest pressure collected so far is not sub-therapeutic), furtherdata points are collected at one or both of the lowest pressure(s) and alower pressure until a sub-therapeutic condition is reached. The lowestSDBIndex from the initial collection periods is also examined, andtypically occurs at the highest pressure P. If the SDB indices for thehigher pressure(s) is not below the therapeutic value (such as, forexample, the highest pressure collected until this point is not yet highenough to be classified as therapeutic), further data points arecollected at the highest and/or higher pressures until an optimaltherapeutic condition is reached as judged from the SDB index. In anembodiment, if the SDBIndex for a certain pressure is higher than athreshold (such as, for example, 10), the process can increase thepressure by a higher change in pressure than would otherwise occur (suchas, for example, jumping from a pressure of 5 cm H₂O to 6 or 7 cm H₂Oinstead of a normal jump to 5 cm H₂O).

The clock recording elapsed duration of a window may be stopped when asession of data (such as, for example about 3 hours of data at onepressure in an embodiment) is interrupted. The interruption can be, forexample, the end of the night, when it is determined that the patienthas awoken, when there is a high leakage rate, or when the patientremoves the mask. Alternatively, the total targeted duration of one datacollection window may be adjusted such that it is terminated anytime theflow becomes invalid or the machine is turned off, provided it isgreater than some minimum threshold (such as, for example, about 2 hoursin an embodiment) but otherwise continue to the target window length. Ifthe collected data is less than the lower threshold, that windowcontinues to accrue on the next time or night when the same pressure isdelivered. In an embodiment, data is collected until a sufficient amountof both sub-therapeutic and therapeutic pressures are collected asjudged by the SDB index.

FIG. 5A illustrates one potential embodiment of the data collection step501. The process can start with an initial pressure (P_(Ini)) asdescribed above. The process then moves to step 553 where a range ofpressures to be evaluated are selected. For example, this can be a rangeincluding predetermined pressure offsets from the initial pressure, suchas, for example, P_(Ini)−P_(low); P_(Ini)+P_(high), etc. The processthen moves to step 555 where pressure level data is collected. At step557, the collected pressure level data is initially analyzed todetermine if the data collection process 501 should be exited. The exitcriteria can include, for example, a determination that all desiredlevels of pressure have been evaluated or that an optimal pressure hasbeen determined.

When sufficient data has been collected, data collection at furtherpressures is ended and the method proceeds to step 503, where thecollected data is analyzed to choose an optimal pressure P_(MNT). In anembodiment, the analysis can be performed on all the data collected inthe multiple intervals and at the multiple pressures. In otherembodiments, the data is passed through an initial screening to confirmthat it represents acceptable measurements. For example, this caninclude determining if the collected data was corrupted by the patient'swakefulness during an individual session or if the leakage rate was toohigh during the session. Alternatively, this initial screening can beperformed at the data collection step 501 in order to ensure thatsufficient acceptable data is collected at each pressure.

Once a set of data is established for analysis, one or more of severalmathematical approaches is used to determine the best pressure P_(MNT)for continuous CPAP that will be prescribed after the MNT titration. Oneapproach to finding the best pressure is to examine the characteristicsof the SDBIndex_(w) vs. Pressure graph, as shown in FIG. 6. Thedetermination of the optimal (or therapeutic) pressure P_(MNT) can bemade using a process that is based on one or more characteristics of theplot of FIG. 6 as will be described in greater detail hereinafter. Eachgraphed point on the plot of FIG. 6 represents the SDBIndex_(w)determined for a given pressure during a data collection interval. Inthe graph of FIG. 6, for example, three intervals of data were collectedfrom each pressure and an SDBIndex value was determined from eachinterval at each pressure and plotted on the graph. Of course it is tobe understood that the calculation can also be performed using anaverage value for each pressure determined from multiple sessionscollected at that pressure.

In an alternative embodiment, the data analysis step 503 may be carriedout in two phases: a first phase including the use of thecharacteristics of the curve to pick a first estimate of the P_(MNT) anda second phase including refining this estimate using additionalcharacteristics of the curve described in greater detail below. Althoughshown in graphical form in the drawings, it is to be understood thatactual graphs may not be determined as part of the data analysis step503. Rather, a series of mathematical equations is performed to generatean output pressure indication using the concepts described herein. Ofcourse, it is to be understood that the set of data can also bedownloaded and/or displayed as a graph for manual review and evaluation.

In an embodiment, as discussed above, calculations of SDBIndex for usein the data collection and analysis portions of the slow titrationprocess 305 are made at a single pressure. During the collection periodfor each pressure there may be external factors that lead to pressurechanges. These periods of pressure deviation can be excluded from theanalysis, as is their contribution to elapsed time of the window. Theexclusion can result in a shortened window of a collection period or itcan result in entire collection periods being excluded. Events leadingto exclusion can include, but are not limited to: periods of high leakleading to a drop in pressure or deterioration of the flow signal,periods of intentional pressure reduction such as triggered by SensAwake(or other patient awake technology that lowers a pressure when it isdetected that a patient is awake) or other comfort technologies intendedto drop therapeutic pressure during periods of arousal (this would notapply to pressure drops with a single breath such as with Respironic'sC-flex™), and periods during a ramp at the beginning of a recordingsession that might particularly occur during the data collectionperiods. As those skilled in the art will understand, these periods mayalso occur when the patient first turns on the CPAP machine if it is setto be used at a low pressure until a sleep onset event occurs.

FIGS. 7-10 depict examples of some specific calculated characteristicsof the collected data according to the disclosure. FIG. 7 is directed toa target crossing process for calculating an optimal pressure. In thisembodiment, the therapeutic pressure P_(MNT) is determined by findingthe point where an extrapolated or fitted curve of the data collectionfor the SDBIndex crosses a certain target level, SDBIndex_(Target). Thistarget level is considered to be the therapeutic level. FIG. 7illustrates an embodiment where the SDBIndex_(Target) is an AHI level,AHI_(Target), and the therapeutic pressure is the pressure at the pointwhere the AHI level crosses the AHI_(Target). For example, this targetlevel can be an AHI of 5. In other embodiments, other SDB indicia can beused. This can include for example, an OI level or other SDB indicia aswould be understood by a person of skill in the art. In an embodiment,the OI_(Target) threshold can be 10.

FIG. 8 is directed to an inflection point calculation. The inflectionpoint is determined from a comparison of SDBIndex_(w) vs. Pressuresimilar to FIG. 6. As those skilled in the art will understand, such adetermination is useful when the SDB indices do not have a simple linearrelationship with the pressure changes and the SDBindex_(w) vs. Pressureindicate a clear point at which further changes in pressure eitherproduce no further improvement or a different (typically flatter)response with further increases in pressure. One technique fordetermining this point is to use a piece-wise linear regression. Thedecision that this has been successfully applied can be based on thestrength of the regression coefficient r². In FIG. 8, the SDBIndex_(w)are OI values and a clear inflection point 801 is illustrated.

FIG. 9 is directed to a Slope Change calculation. The Slope Changecalculation is an evaluation of a pressure via a determination of aslope change in the plot of SDBIndex_(w) vs. Pressure similar to FIG. 6.The Slope Change calculation may be especially useful when the plot ofFIG. 6 follows a curvilinear relationship and no simple inflection pointis found. Specifically, the curve fitted to the data of SDBIndex_(w) vs.Pressure may show a curvilinear relationship such that, above a certainpressure, the negative slope is so small that regardless of pressureincrease, the SDB index does not change significantly. In FIG. 9, theSDBIndex_(w) are OI values and the pressure at point 901 is chosenbecause above that pressure, the negative slope is so small that SDBindex does not change significantly with pressure increases.

FIG. 10 is directed to an SDB Index Peak calculation determination. Thiscalculation compares SDBIndex vs. Pressure (again, similar to FIG. 6) todetermine a pressure at which SDBIndex_(w) assumes its overall minimumor maximum value, irrespective of target, slope or inflection of thecurve. In FIG. 10, for example, the peak minimum pressure is shown atpoint 1001.

Once the characteristics of the curve have been analyzed according toany or a combination of the techniques disclosed above, thecharacteristics can be incorporated into a process to find the optimalpressure P_(MNT). As an example of one such process, a selection of oneof the characteristics above is made to find a first approximation ofP_(MNT), then, in a second pass, examination the characteristics of theentire dataset in the vicinity (up or down) from that pressure isperformed to refine the choice.

In one embodiment of the slow titration process, as shown in FIG. 11,collected data at each pressure for the pressure range may be averagedto display an average curve. From this average curve, a firstapproximation of P_(MNT) may be determined by selecting the pressureP_(Min) associated with a lowest value of the SDBIndex, SDBIndex_(Min),as shown in FIG. 12. In a next step, the data analysis is continued fromP_(Min) and includes only the data collected at the next lowest pressurein a regression line. The SDBIndex is estimated from this datacollection. In a next step, ΔSDBIndex is calculated as the differencebetween SDBIndex at this next lowest pressure and SDBIndex at P_(min).These steps are repeated sequentially for points at decreasing pressuresto calculate the regression line and ΔSDB_(Min). In a next step, it isdetermined if P_(MNT) is sufficiently close to SDBIndex_(Min). That is,if ΔSDBIndex is less than a threshold value, where the threshold valuemay be one of a constant and variable depending on the value ofSDBIndex_(Min) with respect to the therapeutic target. For example, ifSDBIndex_(Min) is less than 10, one could set the threshold for ΔSDB toless than 5. That is, this process determines a compromise of a lowerpressure that may be accepted for minimal increases in SDB that arenevertheless below the therapeutic target. This allows the optimaltherapeutic pressure to the lowest pressure possible withoutsignificantly increasing the SDB of the patient. This results in themost comfortable pressure setting possible for the patient. In anembodiment, if SDBIndex_(Min) is greater than 10 and up to 20, one couldset the threshold for ΔSDB to less than 2. That is, if the SDB is high,the choice of optimal pressure would be lowered only if accompanied byminimal changes in SDBIndex. If SDBIndex_(Min) is greater than 20, onecould set the threshold for ΔSDB less than 1, indicating that the choiceof optimal pressure would only be lowered if there was essentially nopenalty in SDB. FIG. 13 depicts a value of P_(MNT) inferred at apressure below P_(Min) at a point after which SDBIndex does not improvesignificantly upon pressure increase.

Once the data analysis of step 503 has been completed and a firstoptimal pressure has been chosen, the method proceeds to step 505 wherethe acceptability and confidence level of this result may be calculated.The verification step according to the disclosure is particularly usefulin that many patients with SDB show variability of SDB indices at asingle lower pressure, the variability diminishing at higher pressures.One may then, for example, choose the lowest pressure at or above theprevious “optimal P_(MNT)” that has a margin of error still below thetherapeutic threshold for the SDBIndex. Examples of statistics thatcapture the margin of error include: (a) one or two standard deviationsof the SDBIndex at the proposed P_(MNT) pressure related to (orexceeding) the therapeutic threshold for that SDBIndex; (b) the number,or percentage, of points at the proposed P_(MNT) that have an SDBIndexbelow the therapeutic threshold (e.g., whether at P_(MNT) more than 4-6data points lie below OI=10); and (c) the magnitude by which the largestvalue of SDBIndex at the proposed P_(MNT) deviates (exceeds) from thetherapeutic threshold (e.g.; if the “worst” OI seen at the proposedP_(MNT) is >20). FIG. 14 depicts a calculation of the margin of error ateach pressure. The verification step 505 is also useful in that the“best” pressure from a sampled pressure range may still be clinicallyunacceptable, thus evidencing the need for further verification. Ineither of the above situations, further refinement of the P_(MNT) may berequired if the verification step 505 indicates that the chosen P_(MNT)is not acceptable. In this case, either additional MNT data must becollected to lower or raise the P_(MNT) or an alert may be generated forthe clinician to intervene. The alert may include any of a visualdisplay, audible alarm, etc.

After the P_(MNT) has been found, the method proceeds to validation step507 where the CPAP device is locked at the pressure P_(MNT) for apredetermined evaluation period, such as, for example, several nights orweeks. During this period SDB Indices are monitored at the chosenpressure P_(MNT) only. A fractional expression of nights meeting afailure criterion (e.g., wherein SDB per night>a therapeutic value, forexample OI>15) is then calculated. If this fraction rises above athreshold, the multi-night slow titration may be reengaged. This step isperformed to guarantee that the patient has not been incorrectlytitrated and to monitor changes in pressure requirements over time. Forpatients with known significant night-to-night variability in CPAPpressure requirement that is felt to be acceptable, this last step couldbe disabled by the clinician, or the device could automatically switchto another mode of therapy than fixed CPAP (e.g., autotitration).

There are many modifications of the present disclosure which will beapparent to those skilled in the art without departing from the teachingof the present disclosure. For example, a combination of long windowswith moderately abnormal SDB index and short windows with very abnormalSDB index can be used concurrently with the process to “abort andrescue” from data collection at a pressure that is clearly too low. Theembodiments disclosed herein are for illustrative purposes only and arenot intended to describe the bounds of the present disclosure which isto be limited only by the scope of the claims appended hereto.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Any process descriptions, elements, or blocks in the flow diagramsdescribed herein and/or depicted in the attached figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or steps in the process. Alternateimplementations are included within the scope of the embodimentsdescribed herein in which elements or functions may be deleted, executedout of order from that shown or discussed, including substantiallyconcurrently or in reverse order, depending on the functionalityinvolved, as would be understood by those skilled in the art. It willfurther be appreciated that the data and/or components described abovemay be stored on a computer-readable medium and loaded into memory ofthe computing device using a drive mechanism associated with a computerreadable storing the computer executable components such as a CD-ROM,DVD-ROM, memory stick, or network interface. Further, the componentand/or data can be included in a single device or distributed in anymanner. Accordingly, general purpose computing devices may be configuredto implement the processes, algorithms and methodology of the presentdisclosure with the processing and/or execution of the various dataand/or components described above.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure and protected by the following claims.

What is claimed is:
 1. A continuous positive airway pressure (CPAP)system comprising: an air pressure supply arrangement providing at leasta first air pressure to a patient airway during a first time period anda second air pressure during a second time period; a sensor detectingthe flow to the patient's airways to generate flow data; and a hardwareprocessor configured to analyze the flow data from the sensor, theprocessor controlling the air pressure delivered to the airway based ona titration process, the titration process including a fast titrationprocess and a slow titration process; wherein the fast titration processresponds quickly to patient breathing events in order to adjust apressure supplied to a patient; and wherein the slow titration processmaintains a constant pressure over a predetermined period of timewithout responding with pressure changes to individual or small groupsof patient breathing events.
 2. The CPAP system of claim 1, whereinpatient breathing events comprise one or more of apneas, hypopneas,short periods of inspiratory flow limitation or an obstruction index. 3.The CPAP system of claim 1, wherein the predetermined period of time isabout 3 hours.
 4. The CPAP system of claim 1, wherein the predeterminedperiod of time is continuous.
 5. The CPAP system of claim 1, wherein thepredetermined period of time is discontinuous.
 6. The CPAP system ofclaim 1, wherein the predetermined period of time is in a range of about1-8 hours.
 7. The CPAP system of claim 1, wherein the fast titrationprocess responds to individual breathing events.
 8. The CPAP system ofclaim 1, wherein the slow titration process steps through at least threedifferent constant pressures, where each constant pressure is maintainedfor a predetermined period of time.
 9. The CPAP system of claim 8,wherein the three different constant pressures are chosen such that atleast one pressure is sub-therapeutic, at least one is therapeutic andat least one is above-therapeutic.
 10. The CPAP system of claim 8,wherein the three different constant pressures are pressures chosen tobe a predetermined difference from an initial pressure.
 11. The CPAPsystem of claim 1, wherein the slow titration process determines anoptimal therapeutic pressure to be provided for an extended period. 12.The CPAP system of claim 8, wherein the three different constantpressures are chosen such that at least one pressure is sub-therapeutic,at least one is therapeutic and at least one is above-therapeutic. 13.The CPAP system of claim 1, further comprising a memory deviceconfigured to store the analyzed flow data.
 14. The CPAP system of claim1, wherein the processor is configured to calculate a sleep disorderedbreathing index indicating a presence or absence of recurrent individualrespiratory events.
 15. The CPAP system of claim 14, wherein the sleepdisordered breathing index includes one or more of a percentage of timespent in the individual breathing event, percentage of individualbreaths showing inspiratory flow limitation and an indication whether aduration of the individual breathing event exceeds a predeterminedduration.
 16. A method of operating a continuous positive airwaypressure (CPAP) system, the method comprising: supplying a first airpressure to a patient airway during a first time period and a second airpressure during a second time period; sensing air flow to the patient'sairways using a sensor to generate air flow data; and analyzing, using ahardware processor, the air flow data from the sensor; using resultsfrom the analysis to determine an air pressure to be delivered to apatient based on a titration process, the titration process including afast titration process and a slow titration process; wherein the fasttitration process responds quickly to patient breathing events in orderto adjust a pressure supplied to a patient; and wherein the slowtitration process maintains a constant pressure over a predeterminedperiod of time without responding with pressure changes to individual orsmall groups of patient breathing events.
 17. The method of claim 16,wherein patient breathing events comprise one or more of apneas,hypopneas, short periods of inspiratory flow limitation or anobstruction index.
 18. The method of claim 16, wherein the predeterminedperiod of time is about 3 hours.
 19. The method of claim 16, wherein thepredetermined period of time is in a range of about 1-8 hours.
 20. Themethod of claim 16, wherein the fast titration process responds toindividual breathing events.